Lockman SWIRE master catalogue

Checks and diagnostics

from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))

This notebook was run with herschelhelp_internal version: 
04829ed (Thu Nov 2 16:57:19 2017 +0000)

%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix

OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20170710"

master_catalogue_filename = "master_catalogue_lockman-swire_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))

Diagnostics done using: master_catalogue_lockman-swire_20171121.fits

I - Summary of wavelength domains

flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']

venn3(
    [
        np.sum(flag_obs == 4),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 6),
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 5),
        np.sum(flag_obs == 3),
        np.sum(flag_obs == 7)
    ],
    set_labels=('Optical', 'near-IR', 'mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");

venn3(
    [
        np.sum(flag_det[flag_obs == 7] == 4),
        np.sum(flag_det[flag_obs == 7] == 2),
        np.sum(flag_det[flag_obs == 7] == 6),
        np.sum(flag_det[flag_obs == 7] == 1),
        np.sum(flag_det[flag_obs == 7] == 5),
        np.sum(flag_det[flag_obs == 7] == 3),
        np.sum(flag_det[flag_obs == 7] == 7)
    ],
    set_labels=('mid-IR', 'near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

u_bands = ["WFC u",          "CFHT Megacam u"]
g_bands = ["WFC g", "RCS g", "CFHT Megacam g",  "GPC1 g"]
r_bands = ["WFC r", "RCS r", "CFHT Megacam r",  "GPC1 r"]
i_bands = ["WFC i", "RCS i",                    "GPC1 i"]
z_bands = ["WFC z", "RCS z", "CFHT Megacam z",  "GPC1 z"]
y_bands = [         "RCS y",                    "GPC1 y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))

CFHT Megacam u (aperture) - WFC u (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.25
- 1% percentile: -0.94939359664917
- 99% percentile: 3.2226499748230046

CFHT Megacam u (total) - WFC u (total):
- Median: -0.27
- Median Absolute Deviation: 0.21
- 1% percentile: -1.6238471603393554
- 99% percentile: 1.9827452659606981

No sources have both WFC g (aperture) and RCS g (aperture) values.
RCS g (total) - WFC g (total):
- Median: -0.08
- Median Absolute Deviation: 0.18
- 1% percentile: -0.951687240600586
- 99% percentile: 1.1384436035156251

CFHT Megacam g (aperture) - WFC g (aperture):
- Median: 0.11
- Median Absolute Deviation: 0.14
- 1% percentile: -0.5077012062072754
- 99% percentile: 0.8677667236328128

CFHT Megacam g (total) - WFC g (total):
- Median: -0.04
- Median Absolute Deviation: 0.15
- 1% percentile: -0.8756651496887208
- 99% percentile: 1.4227420425414974

GPC1 g (aperture) - WFC g (aperture):
- Median: -0.14
- Median Absolute Deviation: 0.27
- 1% percentile: -2.032780647277832
- 99% percentile: 2.2441247940063653

GPC1 g (total) - WFC g (total):
- Median: -0.03
- Median Absolute Deviation: 0.24
- 1% percentile: -2.5375070190429687
- 99% percentile: 1.7712446594238282

No sources have both RCS g (aperture) and CFHT Megacam g (aperture) values.
CFHT Megacam g (total) - RCS g (total):
- Median: 0.05
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0380180740356444
- 99% percentile: 1.2724963569641101

No sources have both RCS g (aperture) and GPC1 g (aperture) values.
GPC1 g (total) - RCS g (total):
- Median: 0.04
- Median Absolute Deviation: 0.31
- 1% percentile: -3.047450065612793
- 99% percentile: 1.9546246528625488

GPC1 g (aperture) - CFHT Megacam g (aperture):
- Median: -0.33
- Median Absolute Deviation: 0.38
- 1% percentile: -2.9446646881103518
- 99% percentile: 2.185241775512691

GPC1 g (total) - CFHT Megacam g (total):
- Median: 0.01
- Median Absolute Deviation: 0.27
- 1% percentile: -3.233067970275879
- 99% percentile: 1.8606653404235813

No sources have both WFC r (aperture) and RCS r (aperture) values.
RCS r (total) - WFC r (total):
- Median: -0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -1.020458526611328
- 99% percentile: 1.0779574584960954

CFHT Megacam r (aperture) - WFC r (aperture):
- Median: 0.12
- Median Absolute Deviation: 0.15
- 1% percentile: -0.5452780914306641
- 99% percentile: 1.1373992156982422

CFHT Megacam r (total) - WFC r (total):
- Median: -0.08
- Median Absolute Deviation: 0.14
- 1% percentile: -0.9585001754760742
- 99% percentile: 1.5919477844238283

GPC1 r (aperture) - WFC r (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.15
- 1% percentile: -1.294525146484375
- 99% percentile: 1.1388988494873047

GPC1 r (total) - WFC r (total):
- Median: -0.01
- Median Absolute Deviation: 0.14
- 1% percentile: -1.716431884765625
- 99% percentile: 0.9754326629638621

No sources have both RCS r (aperture) and CFHT Megacam r (aperture) values.
CFHT Megacam r (total) - RCS r (total):
- Median: 0.07
- Median Absolute Deviation: 0.17
- 1% percentile: -1.0872343254089354
- 99% percentile: 1.3417478752136218

No sources have both RCS r (aperture) and GPC1 r (aperture) values.
GPC1 r (total) - RCS r (total):
- Median: 0.13
- Median Absolute Deviation: 0.14
- 1% percentile: -1.8319188499450683
- 99% percentile: 1.0489997863769531

GPC1 r (aperture) - CFHT Megacam r (aperture):
- Median: -0.26
- Median Absolute Deviation: 0.25
- 1% percentile: -2.3192443466186523
- 99% percentile: 1.124504203796389

GPC1 r (total) - CFHT Megacam r (total):
- Median: 0.07
- Median Absolute Deviation: 0.14
- 1% percentile: -2.371135654449463
- 99% percentile: 1.0233241271972657

No sources have both WFC i (aperture) and RCS i (aperture) values.
RCS i (total) - WFC i (total):
- Median: -0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -0.9838282966613769
- 99% percentile: 1.3779298400878914

GPC1 i (aperture) - WFC i (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0236112403869628
- 99% percentile: 0.9563374328613277

GPC1 i (total) - WFC i (total):
- Median: 0.06
- Median Absolute Deviation: 0.15
- 1% percentile: -1.1021003723144531
- 99% percentile: 0.9988012313842773

No sources have both RCS i (aperture) and GPC1 i (aperture) values.
GPC1 i (total) - RCS i (total):
- Median: 0.17
- Median Absolute Deviation: 0.12
- 1% percentile: -1.585873508453369
- 99% percentile: 0.8372450828552229

No sources have both WFC z (aperture) and RCS z (aperture) values.
RCS z (total) - WFC z (total):
- Median: -0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -1.0027453994750977
- 99% percentile: 1.752888126373291

CFHT Megacam z (aperture) - WFC z (aperture):
- Median: 0.14
- Median Absolute Deviation: 0.17
- 1% percentile: -0.6399097442626953
- 99% percentile: 3.041360149383542

CFHT Megacam z (total) - WFC z (total):
- Median: -0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.9714330673217774
- 99% percentile: 2.1101235580444326

GPC1 z (aperture) - WFC z (aperture):
- Median: 0.01
- Median Absolute Deviation: 0.15
- 1% percentile: -0.9226207733154297
- 99% percentile: 1.1072298431396488

GPC1 z (total) - WFC z (total):
- Median: 0.09
- Median Absolute Deviation: 0.19
- 1% percentile: -0.9790753555297852
- 99% percentile: 1.4537493515014615

No sources have both RCS z (aperture) and CFHT Megacam z (aperture) values.
CFHT Megacam z (total) - RCS z (total):
- Median: 0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -1.2459503936767578
- 99% percentile: 2.295763130187988

No sources have both RCS z (aperture) and GPC1 z (aperture) values.
GPC1 z (total) - RCS z (total):
- Median: 0.15
- Median Absolute Deviation: 0.18
- 1% percentile: -1.9807025718688964
- 99% percentile: 1.3632040596008301

GPC1 z (aperture) - CFHT Megacam z (aperture):
- Median: -0.22
- Median Absolute Deviation: 0.26
- 1% percentile: -2.7806610012054445
- 99% percentile: 1.4033306312561042

GPC1 z (total) - CFHT Megacam z (total):
- Median: 0.12
- Median Absolute Deviation: 0.18
- 1% percentile: -2.3155046844482423
- 99% percentile: 1.3613292694091803

No sources have both RCS y (aperture) and GPC1 y (aperture) values.
GPC1 y (total) - RCS y (total):
- Median: -0.57
- Median Absolute Deviation: 0.41
- 1% percentile: -3.1696386528015132
- 99% percentile: 1.1870793342590378

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_Lockman-SWIRE.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))

WFC u (aperture) - SDSS u (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422

WFC u (total) - SDSS u (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422

CFHT Megacam u (aperture) - SDSS u (fiberMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7244069290161133
- 99% percentile: 3.033295574188231

CFHT Megacam u (total) - SDSS u (petroMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7244069290161133
- 99% percentile: 3.033295574188231

WFC g (aperture) - SDSS g (fiberMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1531206893920898
- 99% percentile: 0.42618650436401384

WFC g (total) - SDSS g (petroMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1531206893920898
- 99% percentile: 0.42618650436401384

No sources have both SDSS g (fiberMag) and RCS g (aperture) values.
No sources have both SDSS g (petroMag) and RCS g (total) values.
CFHT Megacam g (aperture) - SDSS g (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1000237464904785
- 99% percentile: 1.258941555023195

CFHT Megacam g (total) - SDSS g (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1000237464904785
- 99% percentile: 1.258941555023195

GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.56
- Median Absolute Deviation: 0.26
- 1% percentile: -2.4984278106689453
- 99% percentile: 1.7370917510986335

GPC1 g (total) - SDSS g (petroMag):
- Median: -0.56
- Median Absolute Deviation: 0.26
- 1% percentile: -2.4984278106689453
- 99% percentile: 1.7370917510986335

WFC r (aperture) - SDSS r (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245457077026367
- 99% percentile: 0.32145620346069204

WFC r (total) - SDSS r (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245457077026367
- 99% percentile: 0.32145620346069204

No sources have both SDSS r (fiberMag) and RCS r (aperture) values.
No sources have both SDSS r (petroMag) and RCS r (total) values.
CFHT Megacam r (aperture) - SDSS r (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8565793609619141
- 99% percentile: 2.3280290985107497

CFHT Megacam r (total) - SDSS r (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8565793609619141
- 99% percentile: 2.3280290985107497

GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.43
- Median Absolute Deviation: 0.14
- 1% percentile: -1.5629830932617188
- 99% percentile: 0.6062900543212881

GPC1 r (total) - SDSS r (petroMag):
- Median: -0.43
- Median Absolute Deviation: 0.14
- 1% percentile: -1.5629830932617188
- 99% percentile: 0.6062900543212881

WFC i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962200164794922
- 99% percentile: 0.3547430038452155

WFC i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962200164794922
- 99% percentile: 0.3547430038452155

No sources have both SDSS i (fiberMag) and RCS i (aperture) values.
No sources have both SDSS i (petroMag) and RCS i (total) values.
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1418056106567382
- 99% percentile: 0.3724429321289082

GPC1 i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1418056106567382
- 99% percentile: 0.3724429321289082

WFC z (aperture) - SDSS z (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799

WFC z (total) - SDSS z (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799

No sources have both SDSS z (fiberMag) and RCS z (aperture) values.
No sources have both SDSS z (petroMag) and RCS z (total) values.
CFHT Megacam z (aperture) - SDSS z (fiberMag):
- Median: -0.15
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1121220016479492
- 99% percentile: 2.7666453170776224

CFHT Megacam z (total) - SDSS z (petroMag):
- Median: -0.15
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1121220016479492
- 99% percentile: 2.7666453170776224

GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.32
- Median Absolute Deviation: 0.17
- 1% percentile: -1.4183205986022949
- 99% percentile: 1.0515386199951149

GPC1 z (total) - SDSS z (petroMag):
- Median: -0.32
- Median Absolute Deviation: 0.17
- 1% percentile: -1.4183205986022949
- 99% percentile: 1.0515386199951149

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band	Fν - 0 mag (Jy)
J	1594
H	1024
Ks	666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$

# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)

twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_Lockman-SWIRE.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_ukidss_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))

UKIDSS J (total) - 2MASS J:
- Median: 0.02
- Median Absolute Deviation: 0.06
- 1% percentile: -0.8742653710843861
- 99% percentile: 0.779014798898522

ukidss_ks_like = master_catalogue['m_ukidss_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))

UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.07
- Median Absolute Deviation: 0.08
- 1% percentile: -0.8243777712114841
- 99% percentile: 0.8419315067426179

Keeping only sources with good signal to noise ratio

From here, we are only comparing sources with a signal to noise ratio above 3, i.e. roughly we a magnitude error below 0.3.

To make it easier, we are setting to NaN in the catalogue the magnitudes associated with an error above 0.3 so we can't use these magnitudes after the next cell.

for error_column in [_ for _ in master_catalogue.colnames if _.startswith('merr_')]:
    column = error_column.replace("merr", "m")
    keep_mask = np.isfinite(master_catalogue[error_column])
    keep_mask[keep_mask] &= master_catalogue[keep_mask][error_column] <= 0.3
    master_catalogue[column][~keep_mask] = np.nan

IV - Comparing aperture magnitudes to total ones.

nb_ccplots(
    master_catalogue['m_cfht_megacam_r'],
    master_catalogue['m_ap_cfht_megacam_r'] - master_catalogue['m_cfht_megacam_r'],
    "r total magnitude (CFHT)", "r aperture mag - total mag (CFHT)",
    master_catalogue["stellarity"],
    invert_x=True
)

Number of source used: 2456857 / 4641967 (52.93%)

V - Color-color and magnitude-color plots

nb_ccplots(
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    "g - i (WFC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 78471 / 4641967 (1.69%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_irac1'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)

Number of source used: 129249 / 4641967 (2.78%)

nb_ccplots(
    master_catalogue['m_cfht_megacam_u'] - master_catalogue['m_cfht_megacam_g'],
    master_catalogue['m_cfht_megacam_g'] - master_catalogue['m_cfht_megacam_r'],
    "u - g (CFHT)", "g - r (CFHT)",
    master_catalogue["stellarity"]
)

Number of source used: 2070789 / 4641967 (44.61%)

nb_ccplots(
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_ukidss_j'],
    "J - K (UKIDSS)", "g - J (WFC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 101320 / 4641967 (2.18%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_wfc_z'],
    master_catalogue['m_wfc_z'] - master_catalogue['m_ukidss_j'],
    "i - z (WFC)", "z - J (WFC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 9237 / 4641967 (0.20%)

nb_ccplots(
    master_catalogue['m_irac3'] - master_catalogue['m_irac4'],
    master_catalogue['m_irac1'] - master_catalogue['m_irac2'],
    "IRAC3 - IRAC4", "IRAC1 - IRAC2",
    master_catalogue["stellarity"]
)

Number of source used: 38805 / 4641967 (0.84%)